Individuals, healthcare providers, researchers, and others may seek to collect and analyze large amounts of data from a variety of sources to study, monitor, or improve health; study, monitor, or improve healthcare services; conduct research; or make advances in therapies. But tools for collecting and analyzing large amounts of data from a variety of sources are lacking, as are tools for sharing related information.
Managing the interpretation of data and identifying markers for various health states can be difficult. The amount of data, types of data, or real-time processing strain may be too large for systems to handle. The analysis or reporting may be too regimented or restricted to a specific path, which makes changes difficult to implement and alternate hypotheses burdensome to test. Furthermore, there is no easy way to share data, methodologies, processes, configurations, algorithms, interpretations, and results among studies, among users, or across different scientific fields.
Current strategies for collecting data from numerous subjects, monitoring health status, and providing guidance to subjects on adhering to a therapy plan suffer from inflexibility. Current systems are designed to deploy a specific therapy plan, and they cannot identify new markers, or adapt markers to identify new therapeutics. Other systems may provide some flexibility in collecting data, but only if a user is capable of complex coding techniques.
Other systems offer unidirectional data migration, with hard-coded sensors, devices, and analyses. Other systems may offer processing of only stored data or real-time data, but not both. And often the data is obtained from only a single source.
Currently, data (e.g., baseline data) is first collected in a manner that is protocolized to match a specific model, and, then, after reviewing the data against the model, a new protocol is developed to collect further data and compare. This cycle repeats until a discovery is made, but it can take years to develop the protocol, modify the solution to aggregate the information, and then compare the results to the developed model to confirm whether the findings yield a level of precision required for investigatory trials of new pharmaceuticals, medical devices, and therapies. There exists a need for systems and methods to allow new markers and refined markers to be modeled and identified, to encompass the use of data from a variety of sources, to provide readily adaptable and reconfigurable analysis and reporting, to allow the sharing of information among users and/or subjects, and to facilitate the application of interventions. There exists a need for systems with bidirectional configurability; soft-coded formulaic re-configurability; the ability to process both real-time and stored data; the ability to test configurations; the ability to use predefined and/or configurable expressions; and the ability to collaborate and share data, workbooks, and expressions in a way that can be adapted and improved upon.
The disclosure presented herein addresses these and other problems of current systems.